K-Nearest Neighbors(KNN)
is a non-parametric method employed in classification and regression problems [1]}. It does not use the training data points to perform any generalization. Thus, the KNN's training phase is much faster than other classification algorithms. In KNN, K represents the number of nearest neighbors, which is the core factor in deciding a data point's label (i.e., class). This technique finds the K closest neighbors of a target point using distance measures (e.g., Euclidean distance). Then, each neighbor votes for their class, and the class with the most votes is taken as the prediction.
